Powerful quantitative forecasting models,
by the first profitable AI lab
Historically, quantitative models—such as trading models—are domain specific. Brilliant people spend their best years testing features, tuning hyperparameters, and iterating architectures within a narrow domain.
But scale is the panacea: large models will find patterns people, and specialized models, could not. Forecasting generalizes.
Automating Iteration
LLMs embedded in optimization loops, evaluated with quantitative metrics, can automate the build-test-improve modeling cycle. Think AlphaEvolve for forecasting problems.
Sample-Efficient General Models
Unlike existing forecasting models, our models leverage data from across contexts, and rely less on human intuition.
Why It Matters
For now, our models trade.
Science is, Ian Hacking writes, the taming of chance. It is the process of iteratively updating priors. Better forecasting improves our ability to select interesting experiments (roughly those with greatest expected uncertainty reduction).
Backed by YC & others
or email founders@zoaresearch.com